Modern search advertising systems monetize search by showing a set of sponsored links, representing advertisements, deemed relevant to the search term and ordered using simple greedy algorithms to capture as much revenue as possible. Typically, the display of such sponsored search ads may be governed by an auction mechanism whereby advertisers may bid on particular terms for a limited number of ad positions which can be shown with the algorithmic results for the search on a keyword or query. A default candidate set of advertisements ordered according to the rules of the auction mechanism may be selected by choosing the top number of advertisements for the limited number of ad positions.
However, selection of such a default candidate set of advertisements in an implementation of an online keyword auctions system may be at the expense of negatively impacting an optimal auctioneer's objective. For instance, an implementation may choose the highest bidders so that each buyer may continue to participate in each auction as long as a buyer's budget may not be exceeded. Such an implementation may fail to provide the optimal objective for an auctioneer. At some point in the day, a buyer that may be able to bid on a variety of keyword auctions may actually spend the entire daily budget as the highest bidder on frequently occurring keywords, and thereby be removed as an available buyer for bidding on less frequently occurring keywords. Thus, this greedy approach may also result in removing more buyers from auctions as the day progresses than may be necessary considering pricing and frequency of keywords over the course of a day.
What is needed is a system and method that may provide a framework that may be used to optimize various objectives of an online auctioneer. Such a system and method should be able to support an auctioneer's objective to maximize revenue, to exclude bidders under certain conditions, and/or to maximize overall utility value of the auctioned keywords to the bidders.